A metadata management strategy based on event-classification in intelligent transportation system

Abstract

With the explosive growth of data information, the object-oriented storage system has been widely used. This paper proposed a metadata management strategy based on Distributed File System-Ceph in terms of event classification, taking advantage of the characteristics of data in urban traffic system. The large amount of data with a wide variety of sources and data types was first classified by machine learning, and a classification model was established. Then, improvements on load balancing were made to the existing Ceph Load Balancing Strategy of metadata partition. The metadata partitioning is to assign and migrate metadata obtained from the event classification model to the target server chosen by the fuzzy optimum method. Experimental results show that the proposed load balancing strategy based on event classification can not only make the overall load of the metadata servers in a relatively stable state but also make the migration times less than that of other algorithms. The extra overhead of the system is also reduced.